ポイント単位で洗練可能な意味マップによる空間理解
Point-Level Refinable Semantic Mapping for Spatial Understanding
各物体点に信頼度スコアを持たせ、多視点の時間的証拠でオンライン更新することで、動的環境でも誤りや古い点を継続的に除去・洗練する物体中心の3D意味マッピング手法を提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Taimeng Fu, Yi Du, Zitong Zhan, Qiwei Du, Shaoshu Su, Zhipeng Zhao, Chen Wang
分類: cs.RO
原文アブストラクト
Open-vocabulary 3D semantic maps have emerged as a key spatial memory representation for long-horizon robot autonomy, enabling robots to reason about objects, their spatial relationships, and past observations across time. However, existing object-centric semantic maps are primarily designed for static environments and rely on an append-only point integration strategy, where newly observed points are accumulated but previously integrated points are rarely reevaluated. Consequently, perception errors accumulate over time, and outdated points persist after objects move or disappear. Although recent dynamic mapping methods update object-level representations in changing scenes, they do not explicitly revise noisy or obsolete points within each object. We propose an online refinable semantic mapping framework that continuously refines object representations through probabilistic point-level confidence updating. Each object point maintains a confidence score that is updated online using multi-view temporal evidence: consistent observations reinforce reliable points, while inconsistent segmentation masks, depth measurements, and visibility cues suppress spurious and outdated points. Combined with object tracking and re-identification to preserve instance identities under motion and relocation, our framework enables object-centric semantic maps to be continuously refined rather than monotonically accumulated. Experiments demonstrate improved mapping accuracy over the previous method by 3.46% on Replica and 2.55% on ScanNet, show robustness in dynamic environments and under perception noise, and provide an effective spatial memory for object navigation and embodied question-answering with success rates of 82.4% and 76.7%.